“Pattern recognition” refers to processing and analyzing information (numerical, textual, and logical relationship) on various forms of representative things and phenomena to describe, identify, categorize, and interpret such things and phenomena. As computer technology has been developing, computers have begun to be applied to pattern recognition to identify and categorize events or processes. The identified events or processes can include concrete objects such as characters, sounds, or pictures, or the identified events or processes can include abstract objects such as statuses or degrees.
For example, computers are being used to perform character recognition. Using Optical Character Recognition as an example, Optical Character Recognition (OCR) refers to a process whereby electronic equipment (for example, a scanner or a digital camera) examines characters printed on paper and determines the character's shapes by detecting patterns of darkness and brightness and then uses a character recognition method to translate the determined shapes into computer characters. In other words, text materials are scanned to generate image files and then the image files are analyzed to acquire character and layout information. The majority of text characters can be recognized using OCR.
However, the current application of computers for character recognition still has some limitations. For example, existing OCR technology often does not recognize forms in the image files scanned from the text materials very accurately. When the computer encounters a form, existing OCR technology often produces garbage characters and is unable to correctly identify the form.